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Data Scientist 4 at Lam Research

Fremont, California, United States -

Full Time

Start Date

Immediate

Expiry Date

19 Feb, 26

Salary

0.0

Posted On

21 Nov, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Data Analysis, Machine Learning, Deep Learning, AI, Statistics, Optimization, Python, MLOps, Feature Engineering, Model Evaluation, Cross-Functional Collaboration, Bayesian Statistics, Process Engineering, Numerical Simulation, Linux, TensorFlow

Industry

Semiconductor Manufacturing

Description

Analyze large, complex datasets from diverse sources to uncover insights and identify opportunities for innovation. Design, build, and deploy robust machine learning models with meaningful uncertainty quantification. Perform rigorous data engineering and model evaluation, including feature engineering, hyperparameter tuning, and model selection. Collaborate with engineering teams to integrate models into production codebases, promoting best practices in code quality and maintainability. Communicate findings and technical results clearly to both technical and non-technical stakeholders. Master's degree with 6+ years of experience or Ph.D. with 3+ years in Computer Science, Engineering, Physics, Applied Mathematics, Statistics, or a related quantitative field. Machine Learning Expertise: Strong theoretical foundation and hands-on experience in ML algorithms, deep learning, AI, statistics, or optimization. Programming Skills: Proficient in Python, with motivation to write efficient, maintainable, testable, and well-documented code. ML Frameworks: Experience with modern ML frameworks such as PyTorch, JAX, or TensorFlow. Problem Solving: Demonstrated analytical and critical thinking skills, with a track record of delivering impactful R&D solutions. Team Collaboration: Proven success working in cross-functional teams with strong execution and communication skills. Domain expertise in semiconductor engineering, Bayesian statistics, process engineering, multi-physics modeling, or numerical simulation. Familiarity with Linux/Unix operating systems. Experience with MLOps tools and principles (e.g., Docker, CI/CD pipelines).

Responsibilities

Analyze large, complex datasets to uncover insights and identify opportunities for innovation. Design, build, and deploy machine learning models while collaborating with engineering teams to integrate models into production codebases.